Overview

Dataset statistics

Number of variables14
Number of observations279495
Missing cells0
Missing cells (%)0.0%
Duplicate rows35995
Duplicate rows (%)12.9%
Total size in memory29.9 MiB
Average record size in memory112.0 B

Variable types

Numeric9
Categorical5

Alerts

Dataset has 35995 (12.9%) duplicate rowsDuplicates
product_weight_g is highly correlated with product_volume and 1 other fieldsHigh correlation
payment_installments is highly correlated with payment_type_encodedHigh correlation
product_volume is highly correlated with product_weight_gHigh correlation
total_payment is highly correlated with product_weight_gHigh correlation
payment_type_encoded is highly correlated with payment_installmentsHigh correlation
product_weight_g is highly correlated with product_volumeHigh correlation
product_volume is highly correlated with product_weight_gHigh correlation
product_weight_g is highly correlated with product_volumeHigh correlation
product_volume is highly correlated with product_weight_gHigh correlation
product_weight_g is highly correlated with product_volumeHigh correlation
product_volume is highly correlated with product_weight_gHigh correlation
review_score is uniformly distributed Uniform

Reproduction

Analysis started2022-04-13 08:11:10.537294
Analysis finished2022-04-13 08:12:16.025546
Duration1 minute and 5.49 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

order_item_id
Real number (ℝ≥0)

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.027267035
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.1 MiB
2022-04-13T13:42:16.443139image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile1
Maximum7
Range6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.1915702753
Coefficient of variation (CV)0.1864853721
Kurtosis129.3629705
Mean1.027267035
Median Absolute Deviation (MAD)0
Skewness9.363184054
Sum287116
Variance0.03669917038
MonotonicityNot monotonic
2022-04-13T13:42:16.597143image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
1272951
97.7%
25701
 
2.0%
3674
 
0.2%
4137
 
< 0.1%
713
 
< 0.1%
512
 
< 0.1%
67
 
< 0.1%
ValueCountFrequency (%)
1272951
97.7%
25701
 
2.0%
3674
 
0.2%
4137
 
< 0.1%
512
 
< 0.1%
67
 
< 0.1%
713
 
< 0.1%
ValueCountFrequency (%)
713
 
< 0.1%
67
 
< 0.1%
512
 
< 0.1%
4137
 
< 0.1%
3674
 
0.2%
25701
 
2.0%
1272951
97.7%

product_weight_g
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2170
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2206.175746
Minimum0
Maximum40425
Zeros18
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size2.1 MiB
2022-04-13T13:42:16.789154image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile125
Q1300
median700
Q31850
95-th percentile10250
Maximum40425
Range40425
Interquartile range (IQR)1550

Descriptive statistics

Standard deviation3938.01908
Coefficient of variation (CV)1.784997903
Kurtosis14.94947563
Mean2206.175746
Median Absolute Deviation (MAD)500
Skewness3.474155103
Sum616615090
Variance15507994.27
MonotonicityNot monotonic
2022-04-13T13:42:17.052142image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20016634
 
6.0%
15012904
 
4.6%
25011404
 
4.1%
30010820
 
3.9%
1008455
 
3.0%
4008052
 
2.9%
3507981
 
2.9%
5006650
 
2.4%
6006564
 
2.3%
7004910
 
1.8%
Other values (2160)185121
66.2%
ValueCountFrequency (%)
018
 
< 0.1%
215
 
< 0.1%
255
 
< 0.1%
502484
0.9%
535
 
< 0.1%
541
 
< 0.1%
5510
 
< 0.1%
584
 
< 0.1%
6022
 
< 0.1%
614
 
< 0.1%
ValueCountFrequency (%)
404256
 
< 0.1%
30000869
0.3%
298001
 
< 0.1%
297504
 
< 0.1%
297005
 
< 0.1%
2960017
 
< 0.1%
2950017
 
< 0.1%
292501
 
< 0.1%
291501
 
< 0.1%
291001
 
< 0.1%

payment_installments
Real number (ℝ≥0)

HIGH CORRELATION

Distinct24
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.972443156
Minimum0
Maximum24
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size2.1 MiB
2022-04-13T13:42:17.262148image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median2
Q34
95-th percentile10
Maximum24
Range24
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.738672072
Coefficient of variation (CV)0.9213538925
Kurtosis2.567251957
Mean2.972443156
Median Absolute Deviation (MAD)1
Skewness1.602498653
Sum830783
Variance7.50032472
MonotonicityNot monotonic
2022-04-13T13:42:17.484151image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
1133203
47.7%
234537
 
12.4%
330267
 
10.8%
420221
 
7.2%
1015403
 
5.5%
515365
 
5.5%
812198
 
4.4%
610902
 
3.9%
74673
 
1.7%
91745
 
0.6%
Other values (14)981
 
0.4%
ValueCountFrequency (%)
02
 
< 0.1%
1133203
47.7%
234537
 
12.4%
330267
 
10.8%
420221
 
7.2%
515365
 
5.5%
610902
 
3.9%
74673
 
1.7%
812198
 
4.4%
91745
 
0.6%
ValueCountFrequency (%)
2481
 
< 0.1%
237
 
< 0.1%
224
 
< 0.1%
2110
 
< 0.1%
2043
 
< 0.1%
1877
 
< 0.1%
1725
 
< 0.1%
1614
 
< 0.1%
15212
0.1%
1426
 
< 0.1%
Distinct141
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.65555377
Minimum0
Maximum208
Zeros34
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size2.1 MiB
2022-04-13T13:42:17.701168image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q17
median12
Q320
95-th percentile36
Maximum208
Range208
Interquartile range (IQR)13

Descriptive statistics

Standard deviation11.30220541
Coefficient of variation (CV)0.7711892425
Kurtosis23.24753891
Mean14.65555377
Median Absolute Deviation (MAD)6
Skewness2.908677768
Sum4096154
Variance127.7398471
MonotonicityNot monotonic
2022-04-13T13:42:17.897149image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
718318
 
6.6%
816401
 
5.9%
616049
 
5.7%
915054
 
5.4%
1014124
 
5.1%
513776
 
4.9%
1113677
 
4.9%
1211908
 
4.3%
1311680
 
4.2%
410781
 
3.9%
Other values (131)137727
49.3%
ValueCountFrequency (%)
034
 
< 0.1%
13282
 
1.2%
26966
 
2.5%
38713
3.1%
410781
3.9%
513776
4.9%
616049
5.7%
718318
6.6%
816401
5.9%
915054
5.4%
ValueCountFrequency (%)
20810
< 0.1%
1951
 
< 0.1%
19410
< 0.1%
1915
< 0.1%
1899
< 0.1%
1881
 
< 0.1%
18710
< 0.1%
1821
 
< 0.1%
1814
 
< 0.1%
1753
 
< 0.1%

product_volume
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct4410
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15856.43936
Minimum168
Maximum296208
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.1 MiB
2022-04-13T13:42:18.156178image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum168
5-th percentile816
Q12816
median6720
Q319344
95-th percentile60000
Maximum296208
Range296040
Interquartile range (IQR)16528

Descriptive statistics

Standard deviation24478.31361
Coefficient of variation (CV)1.543745923
Kurtosis23.40630858
Mean15856.43936
Median Absolute Deviation (MAD)4952
Skewness3.943044266
Sum4431795518
Variance599187837.3
MonotonicityNot monotonic
2022-04-13T13:42:18.391144image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
80006634
 
2.4%
3524671
 
1.7%
6403298
 
1.2%
8163282
 
1.2%
40963005
 
1.1%
236252570
 
0.9%
198002480
 
0.9%
270002366
 
0.8%
200002296
 
0.8%
48002277
 
0.8%
Other values (4400)246616
88.2%
ValueCountFrequency (%)
1681
 
< 0.1%
2881
 
< 0.1%
3524671
1.7%
37410
 
< 0.1%
3781
 
< 0.1%
38451
 
< 0.1%
39629
 
< 0.1%
4082
 
< 0.1%
41613
 
< 0.1%
4184
 
< 0.1%
ValueCountFrequency (%)
2962085
 
< 0.1%
29400023
< 0.1%
2937061
 
< 0.1%
2880009
 
< 0.1%
2879801
 
< 0.1%
2851381
 
< 0.1%
2827505
 
< 0.1%
2812321
 
< 0.1%
2775502
 
< 0.1%
2746256
 
< 0.1%

customer_seller_distance
Real number (ℝ≥0)

Distinct2914
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean631.45028
Minimum0
Maximum8736
Zeros111
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size2.1 MiB
2022-04-13T13:42:18.629147image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile17
Q1226
median455
Q3822
95-th percentile2131
Maximum8736
Range8736
Interquartile range (IQR)596

Descriptive statistics

Standard deviation614.339412
Coefficient of variation (CV)0.9729022719
Kurtosis2.823946982
Mean631.45028
Median Absolute Deviation (MAD)301
Skewness1.630010805
Sum176487196
Variance377412.9131
MonotonicityNot monotonic
2022-04-13T13:42:18.818149image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
141080
 
0.4%
161078
 
0.4%
131050
 
0.4%
151024
 
0.4%
171017
 
0.4%
18995
 
0.4%
11985
 
0.4%
10978
 
0.3%
20970
 
0.3%
23954
 
0.3%
Other values (2904)269364
96.4%
ValueCountFrequency (%)
0111
 
< 0.1%
1267
 
0.1%
2439
0.2%
3495
0.2%
4627
0.2%
5629
0.2%
6723
0.3%
7818
0.3%
8937
0.3%
9805
0.3%
ValueCountFrequency (%)
87362
 
< 0.1%
86771
 
< 0.1%
80255
< 0.1%
79631
 
< 0.1%
35776
< 0.1%
33971
 
< 0.1%
33858
< 0.1%
33812
 
< 0.1%
33781
 
< 0.1%
33571
 
< 0.1%
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.1 MiB
2018
152714 
2017
125836 
2016
 
945

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2018
2nd row2018
3rd row2017
4th row2018
5th row2017

Common Values

ValueCountFrequency (%)
2018152714
54.6%
2017125836
45.0%
2016945
 
0.3%

Length

2022-04-13T13:42:18.989141image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-13T13:42:19.127179image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
2018152714
54.6%
2017125836
45.0%
2016945
 
0.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

total_payment
Real number (ℝ≥0)

HIGH CORRELATION

Distinct1515
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean145.0557506
Minimum7
Maximum6929
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.1 MiB
2022-04-13T13:42:19.342142image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile31
Q157
median96
Q3163
95-th percentile397
Maximum6929
Range6922
Interquartile range (IQR)106

Descriptive statistics

Standard deviation193.2919372
Coefficient of variation (CV)1.332535501
Kurtosis79.42869442
Mean145.0557506
Median Absolute Deviation (MAD)47
Skewness6.746707328
Sum40542357
Variance37361.773
MonotonicityNot monotonic
2022-04-13T13:42:19.562154image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
352752
 
1.0%
372748
 
1.0%
652653
 
0.9%
452631
 
0.9%
472494
 
0.9%
642463
 
0.9%
362432
 
0.9%
772385
 
0.9%
662381
 
0.9%
572314
 
0.8%
Other values (1505)254242
91.0%
ValueCountFrequency (%)
717
 
< 0.1%
95
 
< 0.1%
108
 
< 0.1%
1124
 
< 0.1%
128
 
< 0.1%
1384
 
< 0.1%
14156
0.1%
1573
 
< 0.1%
16133
< 0.1%
17284
0.1%
ValueCountFrequency (%)
69291
 
< 0.1%
67261
 
< 0.1%
49501
 
< 0.1%
47641
 
< 0.1%
46811
 
< 0.1%
45131
 
< 0.1%
419419
< 0.1%
41751
 
< 0.1%
40341
 
< 0.1%
40161
 
< 0.1%

order_status_encoded
Real number (ℝ≥0)

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean90131.9164
Minimum2
Maximum94019
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.1 MiB
2022-04-13T13:42:19.778141image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile94019
Q194019
median94019
Q394019
95-th percentile94019
Maximum94019
Range94017
Interquartile range (IQR)0

Descriptive statistics

Standard deviation18648.32689
Coefficient of variation (CV)0.2069003704
Kurtosis19.06078671
Mean90131.9164
Median Absolute Deviation (MAD)0
Skewness-4.589152056
Sum2.519141997 × 1010
Variance347760095.8
MonotonicityNot monotonic
2022-04-13T13:42:19.902148image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
94019267857
95.8%
10025737
 
2.1%
4262371
 
0.8%
2981790
 
0.6%
2831698
 
0.6%
633
 
< 0.1%
29
 
< 0.1%
ValueCountFrequency (%)
29
 
< 0.1%
633
 
< 0.1%
2831698
 
0.6%
2981790
 
0.6%
4262371
 
0.8%
10025737
 
2.1%
94019267857
95.8%
ValueCountFrequency (%)
94019267857
95.8%
10025737
 
2.1%
4262371
 
0.8%
2981790
 
0.6%
2831698
 
0.6%
633
 
< 0.1%
29
 
< 0.1%

payment_type_encoded
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.1 MiB
72878
211698 
19111
55831 
2566
 
7851
1481
 
4115

Length

Max length5
Median length5
Mean length4.95718707
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row72878
2nd row72878
3rd row2566
4th row72878
5th row72878

Common Values

ValueCountFrequency (%)
72878211698
75.7%
1911155831
 
20.0%
25667851
 
2.8%
14814115
 
1.5%

Length

2022-04-13T13:42:20.046147image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-13T13:42:20.152166image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
72878211698
75.7%
1911155831
 
20.0%
25667851
 
2.8%
14814115
 
1.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Distinct68
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5035.5705
Minimum2
Maximum9217
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.1 MiB
2022-04-13T13:42:20.356150image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile343
Q13124
median5540
Q37622
95-th percentile9217
Maximum9217
Range9215
Interquartile range (IQR)4498

Descriptive statistics

Standard deviation2810.698341
Coefficient of variation (CV)0.5581687994
Kurtosis-1.121375352
Mean5035.5705
Median Absolute Deviation (MAD)2082
Skewness-0.08453743093
Sum1407416777
Variance7900025.162
MonotonicityNot monotonic
2022-04-13T13:42:20.562169image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
921729992
 
10.7%
870523616
 
8.4%
762220618
 
7.4%
661020059
 
7.2%
629519501
 
7.0%
554016650
 
6.0%
576916650
 
6.0%
413213287
 
4.8%
384911118
 
4.0%
378810098
 
3.6%
Other values (58)97906
35.0%
ValueCountFrequency (%)
25
 
< 0.1%
611
 
< 0.1%
1117
 
< 0.1%
1217
 
< 0.1%
2150
 
< 0.1%
2375
 
< 0.1%
2444
 
< 0.1%
27154
0.1%
38196
0.1%
39293
0.1%
ValueCountFrequency (%)
921729992
10.7%
870523616
8.4%
762220618
7.4%
661020059
7.2%
629519501
7.0%
576916650
6.0%
554016650
6.0%
413213287
4.8%
384911118
 
4.0%
378810098
 
3.6%

timing_encoded
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.1 MiB
37046
107796 
32966
96853 
21463
61385 
4561
13461 

Length

Max length5
Median length5
Mean length4.951838137
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row21463
2nd row32966
3rd row21463
4th row32966
5th row21463

Common Values

ValueCountFrequency (%)
37046107796
38.6%
3296696853
34.7%
2146361385
22.0%
456113461
 
4.8%

Length

2022-04-13T13:42:20.767168image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-13T13:42:20.867146image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
37046107796
38.6%
3296696853
34.7%
2146361385
22.0%
456113461
 
4.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Seasons_encoded
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.1 MiB
28526
89733 
18601
57780 
20562
54165 
19472
52985 
8875
24832 

Length

Max length5
Median length5
Mean length4.911154046
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row28526
2nd row20562
3rd row19472
4th row19472
5th row28526

Common Values

ValueCountFrequency (%)
2852689733
32.1%
1860157780
20.7%
2056254165
19.4%
1947252985
19.0%
887524832
 
8.9%

Length

2022-04-13T13:42:21.234167image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-13T13:42:21.338146image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
2852689733
32.1%
1860157780
20.7%
2056254165
19.4%
1947252985
19.0%
887524832
 
8.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

review_score
Categorical

UNIFORM

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.1 MiB
1
55899 
2
55899 
3
55899 
4
55899 
5
55899 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
155899
20.0%
255899
20.0%
355899
20.0%
455899
20.0%
555899
20.0%

Length

2022-04-13T13:42:21.466147image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-13T13:42:21.567141image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
155899
20.0%
255899
20.0%
355899
20.0%
455899
20.0%
555899
20.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Interactions

2022-04-13T13:42:11.048458image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T13:41:45.660865image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T13:41:48.938303image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T13:41:52.216298image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T13:41:55.434298image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T13:41:58.720323image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T13:42:01.866459image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T13:42:04.810459image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T13:42:07.902431image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T13:42:11.404460image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T13:41:46.266322image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T13:41:49.245300image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T13:41:52.534321image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T13:41:55.756301image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T13:41:59.060302image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T13:42:02.172460image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T13:42:05.123458image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T13:42:08.199458image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T13:42:11.752459image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T13:41:46.584300image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T13:41:49.596295image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T13:41:52.889297image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T13:41:56.146298image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T13:41:59.472301image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T13:42:02.508434image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T13:42:05.518458image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T13:42:08.515437image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T13:42:12.059460image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T13:41:46.872296image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T13:41:49.938322image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T13:41:53.230294image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T13:41:56.558296image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T13:41:59.796296image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T13:42:02.908459image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T13:42:05.833458image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T13:42:08.890432image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T13:42:12.402433image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T13:41:47.191296image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T13:41:50.250320image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T13:41:53.589294image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T13:41:56.884322image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T13:42:00.178297image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T13:42:03.220434image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T13:42:06.199458image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T13:42:09.186435image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T13:42:12.752458image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T13:41:47.503294image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T13:41:50.670320image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T13:41:53.972300image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T13:41:57.421323image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T13:42:00.557294image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T13:42:03.538431image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T13:42:06.546459image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T13:42:09.530459image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T13:42:13.129459image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T13:41:47.823320image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T13:41:51.086312image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T13:41:54.327300image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T13:41:57.716294image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T13:42:00.851462image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T13:42:03.833455image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T13:42:06.914458image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T13:42:09.895459image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T13:42:13.551433image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T13:41:48.140320image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T13:41:51.438299image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T13:41:54.721299image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T13:41:58.045293image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T13:42:01.200460image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T13:42:04.139460image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T13:42:07.252458image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T13:42:10.187459image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T13:42:13.882459image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T13:41:48.522300image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T13:41:51.826299image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T13:41:55.088303image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T13:41:58.393299image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T13:42:01.535460image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T13:42:04.465459image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T13:42:07.603458image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T13:42:10.742458image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Correlations

2022-04-13T13:42:21.835010image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-04-13T13:42:22.248253image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-04-13T13:42:22.581253image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-04-13T13:42:22.990228image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-04-13T13:42:23.251255image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-04-13T13:42:14.213459image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
A simple visualization of nullity by column.
2022-04-13T13:42:14.868502image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

order_item_idproduct_weight_gpayment_installmentsorder_delivered_customer_time_in_daysproduct_volumecustomer_seller_distanceorder_purchase_yeartotal_paymentorder_status_encodedpayment_type_encodedproduct_category_name_english_encodedtiming_encodedSeasons_encodedreview_score
011475816125408622018929401972878921721463285261
11300123525220181579401972878554032966205621
214001135967151320172794019256675721463194721
31295011031939552018659401972878384932966194721
4115001018200001720171019401972878378821463285261
51200111441052720181399401972878661032966194721
612390031418295232420172609401972878629537046186011
7116317352838201797940191911176222146388751
811050816561060320171159401972878870537046205621
9127501461347532520183019401919111629537046186011

Last rows

order_item_idproduct_weight_gpayment_installmentsorder_delivered_customer_time_in_daysproduct_volumecustomer_seller_distanceorder_purchase_yeartotal_paymentorder_status_encodedpayment_type_encodedproduct_category_name_english_encodedtiming_encodedSeasons_encodedreview_score
279485170018131672720188494019148187054561205625
279486127006246912176020171859401972878870537046205625
27948716002632203132018120940197287816132966205625
279488144031065616023820181999401972878101932966205625
2794891850101415625100620181519401972878384921463205625
2794901500861200090020189849401972878554037046205625
27949112150662979271720183599401972878384921463194725
2794921550106432093420181059401972878384932966205625
279493152501163128053320182199401972878101921463186015
279494190071824185532017359401972878345932966186015

Duplicate rows

Most frequently occurring

order_item_idproduct_weight_gpayment_installmentsorder_delivered_customer_time_in_daysproduct_volumecustomer_seller_distanceorder_purchase_yeartotal_paymentorder_status_encodedpayment_type_encodedproduct_category_name_english_encodedtiming_encodedSeasons_encodedreview_score# duplicates
239571150015197607020185694019728787573296618601238
1012113251194200403201848940191911141322146318601234
849212801265746794201845940191911176223296628526233
206471100021752501419201729940197287857693704628526233
3162916600383937545520176694019728782815370468875233
3351672113570332201825940197287887053296618601232
611110016220886201832940197287828153296628526232
57331200218281626320187594019728787622456128526232
695712501527002520172294019191113432146328526232
9795130052219362035201817594019728785540456128526232